The use of computers and computer networks pervade virtually every business and other enterprise in the modem world. With computers, users generate and receive vast quantities of data that can be stored for a variety of purposes. This storehouse of data can grow at a phenomenal pace and become critically valuable to those who have generated it. Thus, to be successful in today's economy, companies should seek to obtain the most efficient, cost effective, and best performing Information Technology solutions they can afford. Because data storage has become one of the most important components in that Information Technology infrastructure, there is an ever-present need for data storage systems that improve on capacity, speed, reliability, etc.
In a single computer, the primary data storage device is usually a hard drive with a storage capacity measured in gigabytes. Additionally, computers may store data using such devices as CD-ROM drives, floppy disk drives, tape drive, etc. Within a computer network, the computers of the network may also store data on -network servers or other data storage devices, such as those mentioned above, that are accessible through the network. For larger systems with even greater data storage needs, arrays of data storage disks may be added to the network. Such an array of data storage disks is sometimes referred to as a Redundant Array of Independent (or Inexpensive) Disks (RAID).
Storage Area Networks (SANs) are technology being implemented to accommodate high-capacity data storage devices, particularly disk arrays, within a network. Essentially, a SAN is a high-speed network between client devices, such as networked personal computers and servers, and data storage devices, particularly disk arrays. In most cases, a SAN overcomes the limitations and inflexibility of traditional attached data storage.
Where disk arrays and/or a SAN are implemented as a data storage solution, it is important to match the performance of the array or arrays with the data storage needs of the network. This raises the issue of how to determine or predict the performance of a particular data storage configuration. In other words, how can enough of the right performance data be communicated to aid a solution designer in predicting the performance limitations of an array?
The answer can be complex and difficult. There are many factors to consider when looking at the performance of a data storage solution. These include the characteristics of the client devices (e.g., networked computers and servers), the workload, and the disk array itself. Understanding the limitations of the disk array would aid the solution designers and technical consultants, and would also help field engineers as they try to debug or optimize the data storage solution.
In the past, these issues have been addressed on a configuration-by-configuration basis. When one of the almost infinite possible data storage system configurations is implemented and tested, performance data can be documented in, for example, a white paper. However, each such paper gives performance data specific to the data storage system configuration being documented. Thus, it is easy to see why countless such papers exist, one for each tested configuration. However, due to small variations in configuration or operating conditions, one can often find multiple papers on similar configurations with no consensus in the recorded results. Alternatively, there may be no paper available at all on the configuration a designer is considering.